Intelligent Sensor Data Pre-processing Using Continuous Restricted Boltzmann Machine

• Main Author: Suhaimi, Emil Zaidan
• Format: Final Year Project
• Language: English
• Published: Universiti Teknologi PETRONAS 2007
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://utpedia.utp.edu.my/9537/1/2007%20-%20Intelligent%20Sensor%20Data%20Pre-Processing%20using%20Continuous%20Restricted%20Boltzmann%20Machine.pdf; http://utpedia.utp.edu.my/9537/
• Institution: Universiti Teknologi Petronas

The objective of the project is to finda solution to pre-process noisy signalfor sensors in Lab-on-a-Chip (LOC) and System-on-Chip (SOC) technologies. This solution must be able to process continuous-time, analogue sensor signals directly. It must also be amenable to hardware implementation, with low power consumption. This solution is found in the Continuous Restricted Boltzmann Machine (CRBM), which is a type of Artificial Neural Network which exhibits probabilistic and stochastic behavior. CRBM utilizes continuous stochastic neurons, where Gaussian noise is applied to the inputofthe neurons. The noise inputs cause neurons to have continuous-valued, probabilistic outputs. The use ofstochastic neurons in CRBMgives it modelingflexibility that is useful with real data. The training algorithm of CRBM requires only addition c;nd multiplication, which is computationally inexpensive in hardware and software. The ability ofCRBM to model any given data set is shown by training the CRBM on various data sets reflecting real-world data. In this study, CRBM is shown to be suitable to be implemented in LOC andSOC applications aforementioned.